Mapping and Characterization of HCMV-Specific Unconventional HLA-E-Restricted CD8 T Cell Populations and Associated NK and T Cell Responses Using HLA/Peptide Tetramers and Spectral Flow Cytometry

HCMV drives complex and multiple cellular immune responses, which causes a persistent immune imprint in hosts. This study aimed to achieve both a quantitative determination of the frequency for various anti-HCMV immune cell subsets, including CD8 T, γδT, NK cells, and a qualitative analysis of their phenotype. To map the various anti-HCMV cellular responses, we used a combination of three HLApeptide tetramer complexes (HLA-EVMAPRTLIL, HLA-EVMAPRSLLL, and HLA-A2NLVPMVATV) and antibodies for 18 surface markers (CD3, CD4, CD8, CD16, CD19, CD45RA, CD56, CD57, CD158, NKG2A, NKG2C, CCR7, TCRγδ, TCRγδ2, CX3CR1, KLRG1, 2B4, and PD-1) in a 20-color spectral flow cytometry analysis. This immunostaining protocol was applied to PBMCs isolated from HCMV− and HCMV+ individuals. Our workflow allows the efficient determination of events featuring HCMV infection such as CD4/CD8 ratio, CD8 inflation and differentiation, HCMV peptide-specific HLA-EUL40 and HLA-A2pp65CD8 T cells, and expansion of γδT and NK subsets including δ2−γT and memory-like NKG2C+CD57+ NK cells. Each subset can be further characterized by the expression of 2B4, PD-1, KLRG1, CD45RA, CCR7, CD158, and NKG2A to achieve a fine-tuned mapping of HCMV immune responses. This assay should be useful for the analysis and monitoring of T-and NK cell responses to HCMV infection or vaccines.


Introduction
Human cytomegalovirus (HCMV; human herpesvirus 5, HHV5) is the prototype member of β-herpesvirus family and a widespread opportunistic pathogen. In healthy individuals, primary infection is subclinical and is followed by a life-long, persistent infection that is controlled by host immune system [1]. However, HCMV is a major cause of morbidity and mortality in immunocompromised individuals such as transplant recipients and patients with HIV infection. Immune response against HCMV is complex, multifactorial, and includes a set of persistent and virus-specific effector NK and CD8 αβT and γδT cell populations [2][3][4]. These effector cells display cytotoxic functions devoted to eliminating infected cells and preventing further HCMV reactivation [5,6]. HCMV-reactive CD8 αβT cells against viral peptides (pp65, IE1, etc.) presented by conventional MHC class I (HLA-A and HLA-B) molecules have been well characterized [7]. These conventional CD8 T cell responses are usually associated with an efficient control of infection [5]. In addition, CD8 T cell responses bearing αβTCR but recognizing non-classical MHC, and HLA-E molecules presenting peptides derived from HCMV UL40 protein have emerged as non-conventional T cell responses, also observed in HCMV seropositif (HCMV + ) hosts including transplant recipients and healthy individuals [8][9][10]. In a previous study, we have try analysis. For validation, our immunostaining protocol was applied to PBMCs isolated from HCMV-(n = 4) and HCMV+ (n = 4) individuals and cytometry data were analyzed post-acquisition as follows.

Lymphocyte Gating
As an initial gating strategy to discriminate anti-HCMV NK and T cell populations (Figure 1), both forward and side scatter (FSC-Area (A) vs. FSC-Height (H) and SSC-H vs. SSC-A) dot plots were used to exclude doublets and to target singlets only ( Figure 1A). Next, FSC vs. SSC gating was used to identify lymphocytes based on size and granularity. It is often suggested that forward scatter indicates cell size, whereas side scatter relates to the complexity or granularity of the cell. This gating strategy is also used to exclude debris, as they tend to have lower forward scatter levels. They are found at the bottom left corner of the FSC vs. SSC density plot. Next, live cells were selected using Fixable Viability Stain 440UV as a viability marker. This dye reacts with and covalently binds to cell-surface and intracellular amines. Permeable plasma cell membranes, such as those present in necrotic cells, allow for the intracellular diffusion of the dye and covalent binding to higher overall concentrations of amines than in non-permeable live cells. Using the expression for CD3 and γδTCR allows us to determine three lymphocyte subsets: CD3 − γδTCR − cells including mostly B lymphocytes and NK cells, CD3 + γδTCR − cells including mostly αβTCR T cells, and finally, CD3 + γδTCR + cells, which include γδT cells ( Figure 1A). Thus CD3/ γδTCR costaining provides the distribution of these three subsets among PBMC samples issued from HCMV + patients and controls and may be indicative of γδTCR inflation post-infection.

CD4/CD8 Ratio
CD4 and CD8 expression was then examined in the CD3 + γδTCR − lymphocyte population to define the percentage of CD4 + T cells, CD8 + T cells as well as double positive and negative CD3 + T cells ( Figure 1B). As illustrated in Figure 1C, our data show that HCMVindividuals display a higher percentage of CD4 compared to CD8 (mean values: 66.6% vs. 33.4% for CD4 and CD8 T cells, respectively, from HCMV-individuals (n = 4), p < 0.05). In contrast, HCMV + individuals, even at a distance from primary infection, display no significant difference in the percentages of CD4 versus CD8 T cells (mean values: 48.9% vs. 51.1% for CD4 and CD8 T cells, respectively, from four HCMV + individuals), indicative of an HCMV-induced CD8 T cell inflation. Indeed, expansion of the CD8 T cell pool occurs early post-infection and is a hallmark of HCMV and HIV infections [20][21][22].

HCMV Peptide-Specific CD8 T Cell Responses
Three pHLA tetramer complexes were used in conjunction with anti-CD8 antibodies for the immunostaining of conventional and unconventional HCMV peptide-specific CD8 T cell responses. Conventional CD8 T cells were detected using HLA-A*0201(A2)/ pp65 (NLVPMVATV) tetramer complexes, while unconventional HLA-E restricted CD8 T cells were detected using two HLA-E tetramer complexes containing two different UL40 signal peptides (HLA-E/ VMAPRTLIL and HLA-E/ VMAPRSLLL ). Examples of detection using HLA-A2 pp65 and HLA-E UL40 tetramers and CD8 costaining are shown in Figure 1D,E and reveal similar frequency (from 2 to 6% of total CD8 T cells) for both HCMV antigen-specific, HLA-A2 pp65 and HLA-E UL40, CD8 T cells, consistent with our previous studies [11] Thus, CD8/HLA classI/peptide tetramer costaining allows the detection and quantification of HCMV-specific conventional but also unconventional, HLA-E-restricted CD8 T cell populations using spectral flow cytometry. HCMV peptide-specific CD8 T cells stained with pMHC class I tetramers can be further  characterized by immunophenotyping using antibodies for CD45RA, CCR7, CX3CR1,  PD-1, CD56, CD57, CD158, NKG2A, NKG2C, KLRG1, and 2B4. Firstly, costaining for  CD45RA and CCR7 allows us to segregate CD8 T cells according to their differentiation  state: CD45RA + CCR7 + CD8 T cells are defined as naive T cells (TN), CD45RA − CCR7 − as central memory T cells (TCM), CD45RA − CCR7 + as effector memory T cells (TEM), and CD45RA + CCR7 − as terminally differentiated T cells re-expressing CD45RA (TEMRA) (Figure 2A) [23]. Consequently, CD45RA/CCR7 costaining enables a comparative analysis of CD8 T cell differentiation for HCMV peptide-specific CD8 T cells stained with pHLA class I tetramers, such as conventional vs. unconventional (HLA-E-restricted) CD8 T and a comparison between HCMV peptide-specific CD8 T cells and total (tetramer negative) CD8 T cell pool ( Figure 2A). As illustrated in Figure 2A lower panel, and consistent with previous studies [22,23], a majority of CD8 T cells (around 70%) in HCMV + individuals are TEMRA cells expressing CD45RA but not CCR7. Considering HCMV-specific responses, only a part of HLA-A2 pp65 and almost all anti-HCMV HLA-E UL40 CD8 T cells stained with HLA-E UL40 tetramers that persist in HCMV + individuals post-infection display a CD45RA + CCR7 − phenotype and thus belong to TEMRA cells (Figure 2A). Figure 2B provides a quantification of the CD8 differentiation stages in HCMV + versus HCMV − individuals, indicating a trend toward less TN and more TEMRA in HCMV + individuals, which may reflect the impact of HCMV-specific CD8 populations, as previously reported [22,24].

CD8 T Cell Differentiation
from HCMV-(n = 4) and HCMV+ (n = 4) individuals and cytometry data were an post-acquisition as follows.

Lymphocyte Gating
As an initial gating strategy to discriminate anti-HCMV NK and T cell populatio ure 1), both forward and side scatter (FSC-Area (A) vs. FSC-Height (H) and SSC-H v A) dot plots were used to exclude doublets and to target singlets only ( Figure 1A). Ne vs. SSC gating was used to identify lymphocytes based on size and granularity. It suggested that forward scatter indicates cell size, whereas side scatter relates to the com or granularity of the cell. This gating strategy is also used to exclude debris, as they have lower forward scatter levels. They are found at the bottom left corner of the FSC density plot. Next, live cells were selected using Fixable Viability Stain 440UV as a v marker. This dye reacts with and covalently binds to cell-surface and intracellular Permeable plasma cell membranes, such as those present in necrotic cells, allow for th cellular diffusion of the dye and covalent binding to higher overall concentrations of than in non-permeable live cells. Using the expression for CD3 and γδTCR allows us t mine three lymphocyte subsets : CD3 -γδTCRcells including mostly B lymphocytes cells, CD3 + γδTCRcells including mostly αβTCR T cells, and finally, CD3 + γδTCR which include γδT cells ( Figure 1A). Thus CD3/ γδTCR costaining provides the dist of these three subsets among PBMC samples issued from HCMV + patients and contr may be indicative of γδTCR inflation post-infection.   Concomitant costaining with a panel of antibodies was performed to investigate, in a single assay, receptors for T cell activation and inhibition (2B4, PD-1, CD158, NKG2A, NKG2C, and KLRG1), migration (CX3CR1), and cytotoxic and proliferation capacity (CD56, CD57). This antibody panel was used to better characterize and to compare the various T cell subsets according to their differentiation state, as illustrated in Figure 2C. CD8 T cell differentiation from TN to TEMRA is associated with gain and loss of expression for several receptors, as previously reported [23]. Upon differentiation, CD8 T cells acquire both CX3CR1 and the inhibiting receptor KLRG1, which are not expressed on TN but appear on TEM and are coexpressed (80% of cells) on TEMRA. Similarly, the expression of the activating receptor 2B4 progress along differentiation with majority of TEMRA (55%) being 2B4 + . NKG2C and CD158 are expressed or even coexpressed on TEMRA only. Concerning cytotoxic activity, the expression of CD56 is null for CD8 TN and TCM, appears maximal for TEM (18.2%), and then decreases for TEMRA (5.2%), while CD57 appears on TEM (13.7%) and further increases for TEMRA (27.8%). TEM and TEMRA, coexpressing both CD56 and CD57, represent 2.4% and 12.0%, respectively. Only a small portion of CD8 TEMRA express the activating receptor NKG2C (4.84%, and among them, 1.39% coexpress CD158). These data indicate that our workflow is robust enough to provide an accurate phenotype comparison across the four differentiation stages of CD8 T cells, thus enabling us to characterize HCMV-specific CD8 T cell subsets.

Deciphering γδT and Vδ2 − γδT Cells upon HCMV Infection
The γδ T cells are an integral part of the immune response against HCMV [6,25]. Using our protocol, the use of anti-γδTCR antibodies allows the positive selection of lymphocytes expressing both CD3 and an γδTCR, thus excluding conventional CD3 + T lymphocytes that bear conventional αβTCR ( Figure 3A). This gating step provides a quantification for total γδT cells in blood samples from HCMV − and HCMV + hosts ( Figure 3A,B). Consistent with previous studies [6], γδT cells comprise around 10 ± 7% of total CD3 + cells in both HCMV − and HCMV + individuals ( Figure 3B). In a subsequent step, subgating of CD3 + γδT according to the expression of δ2TCR chain and CD8 provided a mean to focus on CD3 + γVδ2 − γδ T cells. In humans, γδ T cells are divided in two subsets, the Vγ9 + Vδ2 + T cells that are found predominantly in the blood and all the other γδ T cells (collectively called Vδ2 − γδ T cells, and mainly composed of Vδ1 + and Vδ3 + T cells) that are primarily located in tissues, particularly in epithelia [6]. HCMV infection leads to a strong increase (in proportion and number) in γδ T cell subsets in the blood circulation, which persisted long term [26]. HCMV induces the expansion of Vδ2 − γδ T cells in the blood, which correlates with the resolution of infection providing evidence for an antiviral function of these subset of γδ T cells [27,28]. Our data illustrate the predominance of Vδ2 − γδ T cells over Vδ2 + γδ T cells in HCMV + hosts with a frequency that ranges between 38% and 97% (mean value: 65.6%) of total CD3 + γδT cells but with large individual variations ( Figure 3C). HCMVinduced γδ T cells mostly express an effector/memory TEMRA phenotype ( Figure 3A) with similarities to the one described for HCMV-specific CD8 + αβ T cells [23]. Subgating on CD45RA/CCR7 indicated divergent differentiation status for Vδ2 − γδ T cells vs. Vδ2 + γδ T cells with almost all Vδ2 − γδ T beeing TEMRA (CD45RA + CCR7 − ), whereas Vδ2 + γδ T cells include mostly less-differentiated T cells in HCMV + individuals. To further characterize HCMV-induced γδ T cells, the phenotype of both subsets was investigated for the immune receptors used for phenotyping CD8 αβT cells. Comparison of expression pattern further highlights phenotype differences between Vδ2 − and Vδ2 + γδ T cells and suggests that coexpression for CX3CR1 and KLRG1 and CD56/CD16 expression featured Vδ2 − γδ T cells ( Figure 3A).

Analysis of HCMV-Induced NK Cell Subsets
In healthy human adults, NK cells comprise 5-15% of circulating lymphocytes; together with T cells and B cells, they are one of the three major lymphoid lineages [29]. Within lymphocytes, NK cells are phenotypically defined as CD56 + cells that do not express T (CD3) or B (CD19) cell lineage markers. In our protocol, sequential gating on CD3 − TCR γδ − cells followed by the exclusion of CD19 + cells allowed us to define CD3 − CD19 -CD56 +/− as NK cells ( Figure 4A,B). Accordingly, the total percentages of NK cells among peripheral lymphocytes were calculated in samples from HCMVand HCMV + individuals and are shown in ( Figure 4C). The expression of CD56 in combination with CD16, the low-affinity Fc γ receptor IIIa, further allows to distinguish different NK cell subsets [30,31]. By examining CD56 and CD16 costaining, we were able to identify NK cells at different stages of differentiation, including the immature CD56 − /CD16 − , the early differentiated CD56 bright CD16 +/− , the mature CD56 dim/bright CD16 + , and the terminally differentiated CD56 − CD16 + NK cell subsets [32,33]. When comparing the frequency of each subset in HCMV − and HCMV + individuals, we found that CD56 dim CD16 + respresent the vast majority of NK cells in both groups ( Figure 4D). HCMV infection triggers the specific expansion of mature CD56 dim CD16 + NK, expressing the CD94/NKG2C activating receptor and coexpressing the CD57 with a high cytotoxic activity. Thus, subgating using CD57 and NKG2C markers was performed for all the samples to calculate the percentages of mature CD56 dim CD16 + CD57 + NKG2C+ in HCMV − and HCMV + hosts, as shown in Figure 4E. Our data indicated that, although no statistically significant difference was achieved due to individual variability, the frequency of mature, memory-like CD56 dim CD16 + CD57 + NKG2C + appears higher in HCMV + compared to HCMVhosts.

Analysis of HCMV-Induced NK Cell Subsets
In healthy human adults, NK cells comprise 5-15% of circulating lymphocytes; together with T cells and B cells, they are one of the three major lymphoid lineages [29]. Within lymphocytes, NK cells are phenotypically defined as CD56 + cells that do not express T (CD3) or B (CD19) cell lineage markers. In our protocol, sequential gating on CD3 -TCR γδcells followed by the exclusion of CD19 + cells allowed us to define CD3 -CD19 -CD56 +/-as NK cells (Figure 4A,B). Accordingly, the total percentages of NK cells among peripheral lymphocytes were calculated in samples from HCMVand HCMV + individuals and are shown in (Figure 4C). The expression of CD56 in combination with CD16, the lowaffinity Fc γ receptor IIIa, further allows to distinguish different NK cell subsets [30,31]. CD56 dim CD16 + NK, expressing the CD94/NKG2C activating receptor and coexpressing the CD57 with a high cytotoxic activity. Thus, subgating using CD57 and NKG2C markers was performed for all the samples to calculate the percentages of mature CD56 dim CD16 + CD57 + NKG2C+ in HCMVand HCMV + hosts, as shown in Figure 4E. Our data indicated that, although no statistically significant difference was achieved due to individual variability, the frequency of mature, memory-like CD56 dim CD16 + CD57 + NKG2C + appears higher in HCMV + compared to HCMVhosts.

Discussion
Flow cytometry provides a high-throughput and cost-effective method of immunophenotyping and immunomonitoring of patients (currently, more than 40 fluorophores are available) on many cells with high-throughput (approx. 10,000 events/s). In contrast to flow cytometry, which uses fluorescent molecules, mass cytometry uses heavy metal tags and time-of-flight mass spectrometry readouts to measure antibody binding to cells [34]. This method allows a much larger number of simultaneous markers than conventional flow cytometry. Mass cytometry, on the other hand, acquires cells at a much lower rate (approx. 300-400 events/s) but with more markers per cell (over 50). Spectral cytometry improves conventional flow cytometry by increasing the number and combination of fluorophores, thereby providing increased flexibility of panel design, as well as incorporating autofluorescence measurement and extraction [19]. By the use of fluorophore-conjugated antibodies, staining, and analysis protocols already established for conventional cytometry, spectral cytometry provides a readily accessible technique [35].
Here, we report on the development of a staining protocol and a staining strategy combining HLA class I/HCMV peptides tetramer complexes and a panel of 18 antibodies to study HCMV-specific immune cell responses. HLA/peptide tetramer staining offers the possibility to detect and quantitate peptide-specific anti-HCMV CD8 T cell populations [16]. The tetramers that we used include HLA-A2pp65 tetramers and HLA-E UL40 tetramers to decipher HLA-E-restricted CD8 T cells induced in the response to HCMV infection. It is important to emphasize that HLA-E UL40 tetramers bind to both NK and T cells. HLA-E is a ligand for the heterodimeric CD94/NKG2A/C receptors [36], which are expressed at high level on NK cells and are also expressed at lower level on CD8 T cells [36,37]. To allow a TCR-specific binding and to avoid the binding of HLA-E UL40 tetramers to CD94/NKG2A and CD94/NKG2C receptors, we performed a CD94 blockade using blocking antibodies as a preliminary step of immunostaining as we previously reported [11,13]. A major result from this study was the efficient detection of both HLA-A2 pp65 and HLA-E UL40 CD8 T cells stained with the tetramers in our experimental conditions using spectral cytometry. In the present study, we found that the frequency of pp65 and UL40 epitope-specific T cells among CD8 T cells was in the range of the frequency that we previously reported with conventional flow cytometry [11]. This result indicates that the binding of pHLA tetramer remains stable during the processes of immunostaining and data acquisition and is strong enough to allow cell detection by spectral flow cytometry. Therefore, pHLA-E tetramers against UL40 HCMV epitopes combined with cell surface markers allow us to study these HCMV-specific CD8 T cell responses in more detail in a large cohort of patients. Previous studies established the emergence of HLA-E-restricted CD8 T cell subsets in autoimmune [38] and infectious diseases [12,39]. Functionally, in some studies, HLA-Erestricted CD8 T cells have been shown to display cytotoxic activities [8,11,39] toward autologous, allogeneic, or infected cells expressing HLA-E such as endothelial cells [40] but also regulatory functions [38], as reported in mice [41]. The role of HLA-E-restricted CD8 T cells in the outcome on HCMV infection is still unknown. Previous analysis of phenotype identified HCMV-specific HLA-E CD8 T as terminally differentiated TEMRA cells expressing CD56 [8,11]. Commonly, there is no known specific surface receptor that leads to HLA-E UL40 CD8 T cell identification within PBMCs that may help to analyze or sort these T cells without using HLA-E tetramers. The phenotypic and molecular characteristics of these CD8+ T cells therefore require further study. To investigate further the role that HLA-E UL40 CD8 T cells may play in the control of HCMV infection, we sought to set up an integrated analysis of the multiple cellular responses induced by the infection. Our panel of antibodies enables the concomitant determination in a single sample of the frequency for a set of anti-HCMV responses such as conventional and unconventional peptide-specific CD8 T cells, total γδT and δ2 − γT cells, immature and mature NK, and memory-like NK cells expressing NKG2C and/or CD57 [6,42]. Each subset can be further characterized for the expression of several markers including 2B4, PD-1, KLRG1, CD45RA, CCR7, CD158, and NKG2A to achieve a fine-tuned analysis of HCMV immune responses. Future applications for this assay include a better knowledge of HCMV infection through the comprehensive analysis of NK and T cell responses to HCMV infection or vaccines and a tool for the stratification of transplanted patients according to risk factors related to HCMV infection [43,44].

Samples and Reagents
Blood samples collected from seronegative (HCMV -) and seropositive (HCMV + ) anonymous healthy volunteers (HV) were obtained from the Etablissement Français du Sang (EFS des Pays de La Loire, Nantes, France) with donors' specific and written informed consent for research use. PBMCs were isolated by Ficoll density gradient (Eurobio, Les Ulis, France) and keep frozen until used. Banked biological samples (PBMCs) from HCMV + kidney transplant recipients were issued from the DIVAT biocollection (CNIL agreement n • 891735, French Health Minister Project n • 02G55). PBMCs from patients who underwent kidney transplantation in the Institute for Transplantation Urology Nephrology (ITUN, CHU de Nantes, France) were prospectively isolated from blood samples, frozen, and stored at the Centre de Ressources Biologiques (CRB, CHU de Nantes, France). PBMCs were thawed before use in RPMI-1640 medium (Gibco, Amarillo, TX, USA) supplemented with 10% human serum (Gibco), 2 mM L-glutamine (Gibco), 100 U/mL penicillin (Gibco), and 0.1 mg/mL streptomycin (Gibco). To set up the present protocol, we used blood samples that we previously tested for the absence or the presence of HCMV peptidespecific CD8 T cells using a set of pHLA tetramers and conventional flow cytometry. Four PBMC samples containing different HCMV peptide-specific CD8 T cells were then selected for the study. These 4 samples were issued from 2 HCMV + healthy donors and from 2 HCMV + kidney transplant recipients. The two groups included 2F/2M and 1F/3M for HCMV − and HCMV+ individuals, respectively. The mean values of ages are 47.5 ± 14.7 and 55 ± 14.7 years for HCMV − and HCMV + , respectively, and thus are not significantly different.

Spectral Flow Cytometry: Immunostaining, Acquisition and Post-Acquisition Data Analysis
The 20-marker panel was optimized for use on a Cytek Aurora (Cytek Biosciences, Fremont, CA, USA) spectral flow cytometry platform with a 5-laser configuration (laser excitation wavelengths: 355 nm, 405 nm, 488 nm, 561 nm, and 640 nm). Before use, titration experiments were carried out to determine the antibody concentration providing highest staining index. For immunophenotyping, cells (1.10 6 PBMCs/well in 96-well plates) were costained using a multistep protocol. PBMCs were washed twice in RPMI and cells and filtered through a 100 µm filter (ThermoFisher, Waltham, MA USA) before immunostaining. PBMCs were then incubated with a viability marker, Fixable Viability Stain 440UV (BD Bioscience) and diluted in PBS (100 µL) for 15 min at 4 • C. PBMCs were washed twice in PBS and centrifugated at 2500 rpm for 2 min at 4 • C. Next, cells were incubated with anti-NKG2A (Biotechne, Noyal-Châtillon-sur-Seiche, France) and anti-NKG2C (BD Bioscience) mAbs for 15 min at 4 • C, diluted in PBS (30 µL) before being incubated in PBS (30µL) for 25 min at 4 • C with blocking anti-CD94 mAb (BD Bioscience) to avoid the binding of HLA-E UL40 tetramer to CD94/NKG2A/C receptors. After a washing step, PBMCs were incubated with APC-labeled -HLA peptide tetramers (50µg/mL in 30µL PBS) for 20 min at RT. After washing, PBMCs were incubated successively for 10 min at 4 • C with 5 cocktails of antibodies diluted in PBS (30µL): cocktail 1 containing Fc-block™ reagent (BD Bioscience) and anti-CCR7, cocktail 2 containing anti-TCRγδ and anti-TCRγδ2, cocktail 3 containing anti-CX3CR1 mAbs alone, cocktail 4 containing anti-CD158, -KLRG1, -2B4, -PD-1 mAbs, and cocktail 5 containing anti-CD4, -CD57, -CD3, -CD45RA, -CD8, -CD56 and -CD16 mAbs. After a washing step, PBMCs were finally incubated with anti-CD19 mAbs for 15 min at 4 • C. All antibodies are listed in Table 1. PBMCs were washed twice and were resuspended in PBS before fluorescence analysis. For our study, a mean value of 40,000 events (viable lymphocytes)/samples were acquired for analysis. The fluorescence intensities were measured with a five-laser Cytek Aurora™ spectral flow cytometer (Cytek Biosciences) using SpectroFlo™ software version 2.2.0 (Cytek Biosciences). Using online fluorescence spectra viewers, we were able to identify 20 fluorophores with distinct signatures that could be used in the panel. The selected fluorophores included BUV395, UV440, BUV496, BUV563, BUV737, BUV805, BV421, VioBlue, BV510, BV570, BV605, BV785, FITC, PerCPeFluor710, PE, AlexaFluor594, PE-Cy7, SparkNir685, APC Fire750, and APC (Table 1). The spectral profile of unstained cells was collected and treated as an independent parameter, which allows the autofluorescence signature to be extracted using the unmixing algorithm. The full emission spectrum of each single-stained sample was performed using compensation beads (OneComp eBeads™, Thermo Fisher) or PBMCs and was used to determine the contribution of each fluorophore in a mixed sample using spectral deconvolution (unmixing) algorithms before experiments. The fluorophore spectral signatures obtained at the cytometer were compared to the gold standard full-spectrum signatures shown in the Aurora fluorochrome guide (https://cytekbio.com/blogs/resources/5l-full-spectrum-cytometry-overview-poster, accessed on 1 November 2021) to ensure fluorophore identity and quality. Post-acquisition, unmixed FCS files were conventionally compensated before the data analysis. The frequency of major immune cell populations was determined using FlowJo™ Software v10 (BD Biosciences) based on manual gating strategies as reported in the results section.

Statistical Analysis
Comparisons between groups were represented as box plots showing median, 25th, and 75th percentile values using GraphPad Prism 8.0 software (GraphPad Software Inc., Sand Diego, CA, USA). Comparisons among groups were performed using non-parametric Wilcoxon-Mann-Whitney tests when suitable. Statistical differences were determined by GraphPad Prism 8.0. A two-sided p value < 0.05 was considered to be statistically significant. p value: * for p < 0.05. Institutional Review Board Statement: Banked biological samples (PBMCs) were issued from the DIVAT biocollection (CNIL agreement n • 891735, French Health Minister Project n • 02G55). This retrospective study was performed according to the guidelines of the local and national ethics committees (CCPRB, CHU de Nantes, France). Blood samples collected from anonymous healthy volunteers (n = 25) were obtained from the Etablissement Français du Sang (EFS Pays de la Loire, Nantes) with donors' specific and written informed consent for research use.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement:
Publicly available datasets were analyzed in this study.